DE102021202526A1 - Computer-implemented method for performing an automated driving function, computer program product, control unit and motor vehicle - Google Patents

Abstract

Described is a method for performing an automated driving function of a motor vehicle by means of at least one algorithm, the algorithm has a detection algorithm that uses an object class library of different object classes to identify objects in the area surrounding the motor vehicle, with a number of basic object classes being selected, traffic information about a route section in front of the motor vehicle is received, at least one additional object class being extracted from the traffic information, the at least one additional object class being selected in addition to the basic object classes for recognizing objects in the area surrounding the motor vehicle.

Description

A computer-implemented method for performing an automated driving function, a computer program product, a control unit and a motor vehicle are described here.

Computer-implemented methods for performing an automated driving function, computer program products, control units and motor vehicles of the type mentioned at the outset are known in the prior art.

Methods, computer program products, motor vehicles and systems of the type mentioned at the outset are known in the prior art. The first semi-automated vehicles (corresponds to SAE Level 2 according to SAE J3016) have reached series maturity in recent years. Automated (corresponds to SAE Level >=3 according to SAE J3016) or autonomously (corresponds to SAE Level 4/5 according to SAE J3016) driving motor vehicles have to react independently to unknown traffic situations with maximum safety based on a variety of specifications, e.g. destination and compliance with current traffic regulations. Since the reality of traffic is highly complex due to the unpredictability of the behavior of other road users, it is considered almost impossible to program corresponding control units in motor vehicles using conventional methods and on the basis of man-made rules.

Instead, it is known to develop algorithms using methods of machine learning or artificial intelligence. On the one hand, such algorithms can react more moderately to complex traffic situations than traditional algorithms. On the other hand, with the help of artificial intelligence, it is in principle possible to further develop the algorithms during the development process and in everyday life through constant learning. Alternatively, a status is frozen by the manufacturer after the end of the training phase in the development process.

From the DE 10 2018 117 777 A1 methods and systems for controlling an autonomous vehicle are described. A sensor fusion system including a sensor system for providing environmental status information and a folded neural network (CNN) is provided. The CNN includes a receiving interface configured to receive the environmental state information from the sensor system, a common convolution layer configured to extract traffic information from the received environmental state information, and a plurality of fully connected layers configured to objects recognize belonging to different object classes based on the extracted traffic information, wherein the object classes include at least one of a road feature class, a static object class and a dynamic object class.

When it comes to object recognition in image data, especially 2D image data, deep neural networks (DNNs) have proven to be superior in terms of their performance compared to traditional image processing methods. The use of such methods in automated driving functions requires fast and efficient processes, since rapid processing of the input data is required to ensure driving safety.

The object is therefore to further develop computer-implemented methods for carrying out an automated driving function, computer program products, control units and motor vehicles of the type mentioned at the outset such that increased computing efficiency can be ensured compared to conventional methods.

The object is achieved by a computer-implemented method for performing an automated driving function according to claim 1, a computer program product according to the independent claim 10, a control unit according to the independent claim 12 and a motor vehicle according to the independent claim 13. Further refinements and developments are the subject of the dependent claims .

1. a) eine Anzahl an Basisobjektklassen (nODD_i) ausgewählt wird, wobei die Anzahl an Basisobjektklassen (nODD_i) kleiner ist als die gesamte Anzahl an Objektklassen (nODD_i, nADD_i) in der Objektklassenbibliothek;
2. b) wenigstens ein Eingangssignal mit Informationen über einen vor dem Kraftfahrzeug liegenden Streckenabschnitt empfangen wird, wobei aus den Informationen wenigstens eine Zusatzobjektklasse (nADD_i) ermittelt wird, wobei die wenigstens eine Zusatzobjektklasse (nADD_i) zusätzlich zu den Basisobjektklassen (nODD_i) zur Erkennung von im Umfeld des Kraftfahrzeugs befindlichen Objekten ausgewählt wird;
3. c) mittels Kraftfahrzeugsensorik Umgebungsinformationen einer Umgebung des Kraftfahrzeugs aufgenommen und an den Detektionsalgorithmus weitergeleitet werden;
4. d) mittels des Detektionsalgorithmus anhand der Umgebungsinformationen unter Anwendung der Basisobjektklassen (nODD_i) und der wenigstens einen Zusatzobjektklasse (nADD_i) in der Umgebung des Kraftfahrzeugs befindliche Objekte erkannt werden und dem Algorithmus entsprechende Objektdaten zur Verfügung gestellt werden.

A computer-implemented method for performing an automated driving function of an autonomously or semi-autonomously driving motor vehicle is described below using at least one algorithm, the algorithm being executed in at least one control unit, the control unit intervening in aggregates of the motor vehicle on the basis of input data, the algorithm being activated has a trained neural network, which is designed for object recognition, a detection algorithm being provided which uses an object class library of different object classes (nODD_i, nADD_i) to recognize objects located in the area surrounding the motor vehicle, wherein

1. a) a number of base object classes (nODD_i) is selected, the number of base object classes (nODD_i) being smaller than the total number of object classes (nODD_i, nADD_i) in the object class library;
2. b) at least one input signal with information about one in front of the motor vehicle the route section is received, at least one additional object class (nADD_i) being determined from the information, the at least one additional object class (nADD_i) being selected in addition to the basic object classes (nODD_i) for detecting objects located in the area surrounding the motor vehicle;
3. c) environmental information of an environment of the motor vehicle is recorded by means of motor vehicle sensors and forwarded to the detection algorithm;
4. d) objects located in the vicinity of the motor vehicle are detected by means of the detection algorithm based on the environmental information using the base object classes (nODD_i) and the at least one additional object class (nADD_i) and the algorithm is provided with corresponding object data.

The control unit can be a separate control unit of the motor vehicle or part of a control unit with additional functions not described here. For reasons of operational safety, a corresponding control unit can have a discrete design. Provision can also be made for the control unit to be configured redundantly or for the method described here to be executed redundantly on one or more computing units.

The detection algorithm can be part of the algorithm executing the method or can be designed separately. This makes it possible to carry out the detection algorithm entirely or partially in a decentralized manner, e.g. in the firmware of a camera used accordingly.

In general, current data relating to the environment can be considered as input data, for example data obtained by means of various sensors. Such sensors can include, for example, cameras, radar, lidar and/or ultrasonic sensors, but also other sensors such as position sensors, e.g. GPS, magnetic field detecting sensors and the like.

Further possible input data are color planning data, which are obtained from a navigation destination, for example, and possibly traffic data, which determine the traffic flow on the route. Further data can be communication data, which is obtained from car-to-car or car-to-infrastructure systems, e.g. on traffic light phases or similar.

There are a number of different objects in road traffic that can be divided into different classes, e.g. other road users in motorized vehicles, road users in non-motorized vehicles, pedestrians, animals, objects, road signs, etc.

In object recognition, it is necessary in the prior art to apply the input data to all object classes in order to achieve a complete classification of the objects in the area surrounding the motor vehicle. This requires a certain computational effort. A reduction in the object classes can speed up the implementation of the method.

It is known that on certain types of roads, certain object types occur rarely or not at all. For example, there should generally be no pedestrians, cyclists or animals on a freeway. However, this cannot be completely ruled out, e.g. it happens again and again that people are on the road, e.g. in the event of an accident. In this respect, it is not possible, for example, to completely deactivate an object class "People" if the motor vehicle in question is on a freeway.

With the method described above, it is nevertheless possible to reduce the number of object classes used and thus achieve a targeted design of the requirements. Because at least one input signal with information about a section of road ahead of the motor vehicle is received, additional object classes can be added to the selected object classes, which may be dependent on the road type, or basic object classes selected for the respective road type.

In the event of an upcoming accident, the information could thus be obtained that there are people on the road in a specific area ahead. This causes at least one additional object class, e.g. people and animals, to be loaded and used for object recognition.

The relevant object data, which can be recorded via the motor vehicle sensor system, is used as input data for the detection algorithm.

In a first further development, it can be provided that the detection algorithm has a deep neural network.

The self-learning neural network can be based on various learning principles, in particular it can use methods of reinforcement learning. Reinforcement learning stands for a number of Machine learning methods in which the neural network autonomously learns a strategy to maximize rewards received. The neural network is not shown which action is best in which situation, but receives a reward at certain times, which can also be negative. Using these rewards, it approximates a utility function that describes the value of a particular state or action.

In addition to an input level and an output level, deep neural networks have at least one hidden level (hidden layer) and are able to analyze and evaluate complex situations.

In a further refinement, it can be provided that the number of basic object classes (n_ODD_i) is selected as a function of the driving situation.

Such driving situations can be route-, time-of-day-, season- and region-specific. For example, it may be appropriate to keep an "animals" object class always loaded when driving at night, since there are frequent deer crossings at night.

Another example is routes that are crossed by railway lines. The corresponding object class will not be used on other routes, so the processing of the input data using an object class “rail vehicles” is not necessary there.

In a further refinement, it can be provided that if at least one of the objects in the surrounding area cannot be recognized using the base object classes (nODD_i) and the at least one additional object class (nADD_i), recognition is carried out using the other known object classes (nODDJ). .

In this way it can be ensured that each object found can be classified correctly, even if it does not correspond to one of the base object classes or additional object classes used.

In a further refinement, it can be provided that the at least one additional object class (nADD_i) is deselected when the motor vehicle leaves the route section.

In this way, the process can be accelerated again after leaving the route section.

In a further refinement, provision can be made for the selection of the basic object classes (nODD_i) to be made depending on the type of route traveled.

On inner-city streets, for example, the probability of people crossing the lane is greater than on freeways, so it makes sense to always use the object class people in inner-city areas.

In a further refinement, it can be provided that the detection algorithm uses detection thresholds for the object classes (nODD_i, nADD_i), with at least one detection threshold for the at least one additional object class determined from the information being lowered compared to a standard value.

In this way, a prioritization of the object classes can be achieved.

In a further refinement, it can be provided that the input signal is transmitted by a receiving system, with the information being traffic information and/or with the input signal containing a sensor signal from a motor vehicle sensor.

As a result, current traffic information can be processed in the first case, for example information relating to people or animals or other objects on a roadway.

In the second case, environmental information can alternatively or additionally be obtained from motor vehicle sensors, e.g. cameras can be used to recognize signs that indicate objects, for example temporary construction site signs or seasonal wildlife crossing signs.

In a further refinement, it can be provided that the input signal contains map data of a map of the surroundings.

Such map data can come from a map stored in the motor vehicle or a map received by means of long-distance communication, the map being enriched with additional information, for example regions with frequent game crossings or regions in which other obstacles, e.g. quarries, moraines or sand drifts, can occasionally occur.

1. a) eine Anzahl an Basisobjektklassen (nODD_i) auszuwählen, wobei die Anzahl an Basisobjektklassen (nODD_i) kleiner ist als die gesamte Anzahl an Objektklassen (nODD_i, nADD_i) in der Objektklassenbibliothek;
2. b) mittels eines Eingangs wenigstens ein Eingangssignal mit Informationen über einen vor dem Kraftfahrzeug liegenden Streckenabschnitt zu empfangen und aus den Informationen wenigstens eine Zusatzobjektklasse (nADD_i) zu ermitteln, wobei die wenigstens eine Zusatzobjektklasse (nADD_i) zusätzlich zu den Basisobjektklassen (nODD_i) zur Erkennung von im Umfeld des Kraftfahrzeugs befindlichen Objekten ausgewählt wird;
3. c) mittels Kraftfahrzeugsensorik Umgebungsinformationen einer Umgebung des Kraftfahrzeugs aufzunehmen und an den Detektionsalgorithmus weiterzuleiten;
4. d) mittels des Detektionsalgorithmus anhand der Umgebungsinformationen unter Anwendung der Basisobjektklassen (nODD_i) und der wenigstens einen Zusatzobjektklasse (nADD_i) in der Umgebung des Kraftfahrzeugs befindliche Objekte zu erkennen und dem Algorithmus entsprechende Objektdaten zur Verfügung zu stellen.

A first independent subject relates to a device for performing an automated driving function of an autonomously or semi-autonomously driving motor vehicle, described by means of at least one algorithm, the algorithm being executed in at least one control unit, wherein the control unit intervenes in aggregates of the motor vehicle on the basis of input data, wherein the algorithm has a trained neural network that is designed for object recognition, wherein a detection algorithm is provided that uses an object class library of different object classes (nODD_i, nADD_i) for detecting im objects located in the surroundings of the motor vehicle, the device being set up to:

1. a) select a number of base object classes (nODD_i), the number of base object classes (nODD_i) being less than the total number of object classes (nODD_i, nADD_i) in the object class library;
2. b) using an input to receive at least one input signal with information about a route section in front of the motor vehicle and to determine at least one additional object class (nADD_i) from the information, the at least one additional object class (nADD_i) in addition to the basic object classes (nODD_i) for recognizing objects located in the environment of the motor vehicle is selected;
3. c) recording environmental information of an environment of the motor vehicle by means of motor vehicle sensors and forwarding it to the detection algorithm;
4. d) using the detection algorithm based on the environmental information using the base object classes (nODD_i) and the at least one additional object class (nADD_i) to recognize objects in the vicinity of the motor vehicle and to provide the algorithm with corresponding object data.

In a first further development, it can be provided that the detection algorithm has a deep neural network.

In a further refinement, it can be provided that the device is set up to select the number of basic object classes (n_ODD_i) depending on the driving situation.

In a further refinement, it can be provided that the device is set up to recognize other known object classes if at least one of the objects in the area cannot be recognized using the base object classes (nODD_i) and the at least one additional object class (nADD_i). (nODD_j) to be made.

In a further refinement, it can be provided that the device is set up to deselect the at least one additional object class (nADD_i) when the motor vehicle leaves the route section.

In a further refinement, it can be provided that the device is set up to make the selection of the basic object classes (nODD_i) depending on the type of route traveled.

In a further refinement, it can be provided that the detection algorithm uses detection thresholds for the object classes (nODD_i, nADD_i), the device being set up to lower at least one detection threshold for the at least one additional object class determined from the information compared to a standard value.

In a further refinement, it can be provided that the input signal is transmitted by a receiving system, with the information being traffic information and/or with the input signal containing a sensor signal from a motor vehicle sensor.

In a further refinement, it can be provided that the input signal contains map data of a map of the surroundings.

Another independent subject relates to a computer program product with a permanent, computer-readable storage medium on which instructions are embedded which, when executed by at least one processing unit, cause the at least one processing unit to be set up to carry out the method of the aforementioned type.

The method can be executed on one or more computing units so that certain method steps are executed on one computing unit and other method steps are executed on at least one other computing unit, with calculated data being able to be transmitted between the computing units if necessary.

The processing unit can be part of the control unit.

In a first further development, it can be provided that the commands have the computer program product module of the type described above.

Another independent item concerns a control unit with a permanent, computer-readable storage medium, wherein a computer program product of the type described above is stored on the storage medium.

Another independent subject relates to a motor vehicle with a control unit of the type described above.

In a first further development it can be provided that the arithmetic unit is part of the control unit.

In a further refinement, provision can be made for the control unit to be networked with environmental sensors and a receiving system.

• 1 ein Kraftfahrzeug, das zum automatisierten bzw. autonomen Fahren eingerichtet ist;
• 2 ein Steuergerät des Kraftfahrzeugs aus 1;
• 3 eine Umgebung mit dem Kraftfahrzeug aus 1, sowie
• 4 ein Ablaufdiagramm des Verfahrens.

Further features and details result from the following description, in which at least one exemplary embodiment is described in detail—if appropriate with reference to the drawing. Described and/or illustrated features form the subject matter alone or in any meaningful combination, possibly also independently of the claims, and in particular can also be the subject matter of one or more separate applications. Identical, similar and/or functionally identical parts are provided with the same reference symbols. They show schematically:

• 1 a motor vehicle that is set up for automated or autonomous driving;
• 2 a control unit of the motor vehicle 1 ;
• 3 an environment with the motor vehicle 1 , such as
• 4 a flowchart of the process.

1 shows a motor vehicle 2 that is set up for automated or autonomous driving.

The motor vehicle 2 has a control device 4 with a computing unit 6 and a memory 8 . In memory 8, a computer program product is stored, which in the following in connection with the 2 until 4 will be described in more detail.

The control unit 4 is connected on the one hand to a series of environmental sensors that allow the current position of the motor vehicle 2 and the respective traffic situation to be detected. These include environmental sensors 10, 11 on the front of motor vehicle 2, environmental sensors 12, 13 on the rear of motor vehicle 2, a camera 14 and a GPS module 16. Environmental sensors 10 to 13 can include radar, lidar and/or ultrasonic sensors, for example .

Furthermore, sensors for detecting the state of motor vehicle 2 are provided, including wheel speed sensors 16, acceleration sensors 18 and pedal sensors 20, which are connected to control unit 4. The current state of motor vehicle 2 can be reliably detected with the aid of this motor vehicle sensor system.

During the operation of the motor vehicle 2, the computing unit 6 has loaded the computer program product stored in the memory 8 and executes it. On the basis of an algorithm and the input signals, the computing unit 6 decides on the control of the motor vehicle 2, which the computing unit 6 would achieve by intervening in the steering 22, engine control 24 and brakes 26, each of which is connected to the control unit 4.

Data from the sensors 10 to 20 are continuously buffered in the memory 8 and discarded after a predetermined period of time so that these environmental data can be made available for further evaluation.

The algorithm was trained according to the procedure described below.

Furthermore, an RDS module 27 (RDS=Radio Data System) is provided, via which traffic and route information is received by means of traffic radio. The RDS module 27 is connected to the control device 4 .

In other embodiments, in addition to or as an alternative to an RDS module, other communication paths can be used to receive relevant traffic information, for example via a car-to-infrastructure or a car-to-car network or a cellular network.

2 shows the control unit 4 1 .

The control device 4 has the computing unit 6 in which a computer program product module 28 (framed in dashed lines) is executed, which has an algorithm 30 for executing an autonomous driving function. The autonomous driving function can be a traffic jam assistant, a brake assistant, a collision warning assistant or the like, for example.

A component of the algorithm 30 is a detection algorithm 32 (framed by dashed lines), which has a neural network 34 .

The neural network 34 is a deep neural network (DNN) that has at least one hidden layer in addition to an input layer and an output layer.

The self-learning neural network 34 learns using methods of reinforcement learning, i. H. In a training phase, the algorithm 30 attempts to obtain rewards for improved behavior in accordance with one or more metrics or benchmarks, ie for improvements in the algorithm 30, by varying the neural network 34. In other embodiments, known learning methods of supervised and unsupervised learning, as well as combinations of these learning methods, can also be used.

The neural network 34 may essentially be a matrix of values, typically called weights, that define a complex filter function that determines the behavior of the algorithm 34 depending on inputs, which are presently received via the environmental sensors 10-20, and control signals generated to control the motor vehicle 2.

The computer program product module 28 can be used both in the motor vehicle 2 and outside of the motor vehicle 2 . It is thus possible to train the computer program product module 28 both in a real environment and in a simulation environment.

The object recognition algorithm 32 accesses an object class library 36 (framed in dashed lines) stored in the memory 8, which has a number of base object classes nODD_i (nODD_1, nODD_2, ... nODD_i) and additional object classes (nADD_1, nADD_2, ... nADD_i).

The relevant object classes nODD_i, nADD_i are loaded by the detection algorithm 32 depending on the situation and used for object recognition. The situational selection of the object classes nODD_i, nADD_i depends, among other things, on the route. Another input variable are traffic reports, which are received by means of the RDS module 27 and from which information relating to a route ahead or a route section ahead is extracted.

3 shows a Autobahn 38.

In addition to the motor vehicle, two motor vehicles 40, 42 are driving on the freeway 38 in different lanes. There is an animal 46 on the roadway on a route section 44 ahead (identified by dashed lines across the freeway 38).

The method described here only loads and uses a selection of base object classes nODD_i that is suitable for freeways, which significantly speeds up the object recognition process compared to conventional methods in which all object classes are used.

From the data obtained via the RDS module 27, it is possible to isolate the information that the animal 46 represents a danger to traffic in the route section 44. On the basis of this information, the detection algorithm 32 can load at least one additional object class nADD_i relating to animals from the memory 8 and use it in the object recognition.

4 shows a flow chart of the method.

At the start of the method, a route for motor vehicle 2 is first retrieved. Route types are extracted from the route of motor vehicle 2 and base object classes are selected on the basis of the route types. The detection then takes place using the detection algorithm with the aid of the selected base object classes.

At the same time, traffic information is called up and analyzed at regular intervals. If there are indications of danger in the traffic information, for example due to objects in the road sections ahead, relevant object classes are extracted in a subsequent step.

The additional object classes are then loaded and detection thresholds for the loaded additional object classes are lowered. Object recognition is then carried out using the base object classes used and the additional object classes.

In order to use suitable base object classes in each case and to avoid an accumulation of additional object classes that have been loaded in the meantime, it is regularly checked whether a certain route section has been left. The route section can be a route section defined by the route or by the traffic information.

Although the subject matter has been illustrated and explained in more detail by exemplary embodiments, the invention is not restricted by the disclosed examples and other variations can be derived therefrom by a person skilled in the art. It is therefore clear that a large number of possible variations exist. It is also clear that exemplary embodiments are only examples which are not to be construed in any way as limiting such as the scope, applications, or configuration of the invention. Rather, the preceding description and the description of the figures enable the person skilled in the art to concretely implement the exemplary embodiments, whereby the person skilled in the art, knowing the disclosed inventive concept, can make a variety of changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without having to Leaving the scope of protection defined by the claims and their legal equivalents, such as a further explanation in the description.

Reference List

2

motor vehicle

4

control unit

6

unit of account

8th

Storage

10

environmental sensor

11

environmental sensor

12

environmental sensor

13

environmental sensor

14

camera

15

GPS module

16

wheel speed sensor

18

accelerometer

20

pedal sensor

22

steering

24

engine control

26

brakes

27

RDS module

28

computer program product module

30

algorithm

32

detection algorithm

34

neural network

36

object class library

38

Freeway

40, 42

motor vehicle

44

track section

46

animal

nODD_i

base feature class

nADD_i

additional object class

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• DE 102018117777 A1 [0005]

Claims (15)

Computer-implemented method for performing an automated driving function of an autonomously or partially autonomously driving motor vehicle (2) using at least one algorithm (30), the algorithm (30) being executed in at least one control unit (4), the control unit (4) being divided into units (22 , 24, 26) of the motor vehicle (2) intervenes on the basis of input data, the algorithm (30) having a trained neural network (34) which is designed for object recognition, a detection algorithm (32) being provided which has an object class library (36) of different object classes (nODD_i, nADD_i) for detecting objects (40, 42, 46) located in the area surrounding the motor vehicle (2), wherein a) a number of base object classes (nODD_i) is selected, the number of base object classes (nODD_i) being smaller than the total number of object classes (nODD_i, nADD_i) in the object class library (36); b) at least one input signal with information about a route section (44) in front of the motor vehicle (2) is received, at least one additional object class (nADD_i) being determined from the information, the at least one additional object class (nADD_i) in addition to the basic object classes (nODD_i ) is selected for detecting objects (40, 42, 46) located in the area surrounding the motor vehicle (2); c) information about the surroundings of the motor vehicle (2) is recorded by means of motor vehicle sensors (10, 11, 12, 13, 14, 15) and forwarded to the detection algorithm (32); d) objects (40, 42, 44) located in the vicinity of the motor vehicle (2) are detected by means of the detection algorithm (32) on the basis of the environmental information using the basic object classes (nODD_i) and the at least one additional object class (nADD_i) and the algorithm (30 ) corresponding object data are made available. Computer-implemented method claim 1 , wherein the detection algorithm (32) comprises a deep neural network (34). Computer-implemented method claim 1 or 2 , The number of basic object classes (n_ODD_i) being selected depending on the driving situation. Computer-implemented method according to one of the preceding claims, wherein, if at least one of the objects (40, 42, 44) in the environment cannot be recognized on the basis of the base object classes (nODD_i) and the at least one additional object class (nADD_i), recognition of the remaining known ones Object classes (nODDJ) is made. Computer-implemented method according to one of the preceding claims, wherein the at least one additional object class (nADD_i) is deselected when the motor vehicle (2) leaves the route section (44). Computer-implemented method according to one of the preceding claims, wherein the selection of the base object classes (nODD_i) is made depending on the type of route traveled. Computer-implemented method according to one of the preceding claims, wherein the detection algorithm (32) detection thresholds for the object classes (nODD_i, nADD_i) applies, wherein at least one detection threshold for the at least one additional object class (nADD_i) extracted from the traffic information is lowered compared to a standard value. Computer-implemented method according to one of the preceding claims, wherein the input signal is transmitted by a receiving system (27), wherein the information is traffic information, and/or wherein the input signal contains a sensor signal of a motor vehicle sensor (14). A computer-implemented method according to any one of the preceding claims, wherein the input signal includes map data of an environment map. Computer program product with a permanent, computer-readable storage medium (8) on which instructions are embedded which, when executed by at least one processing unit (6), cause the at least one processing unit (6) to be set up to carry out the method according to one of the preceding claims. computer program product claim 10 , wherein the instructions the computer program product module (28) according to one of Claims 1 until 9 exhibit. Control unit (4) with a permanent, computer-readable storage medium (8), on the storage medium (8) according to a computer program product claim 10 or 11 is saved. Motor vehicle with a control unit (4). claim 12 . motor vehicle after Claim 13 , wherein the computing unit (6) is part of the control unit (4). Motor vehicle according to one of Claims 13 or 14 , wherein the control unit (4) is networked with environmental sensors (10, 11, 12, 13, 14, 15) and a receiving system (27).

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